Random effect modelling using Bayesian methods
by Jon E. Anderson
International Journal of Services Technology and Management (IJSTM), Vol. 8, No. 4/5, 2007

Abstract: Bayesian methods are increasingly used in a variety of academic disciplines, and important applications in ecology, medicine, management science, operations research and finance. The continued use of these methods depends on understanding the strengths and weaknesses of current Bayesian modelling practice. In this paper, we examine important implementation issues within the context of data analysis with statistical models which contain random effects or unmeasured heterogeneity. Random effect models provide an effective way to incorporate sources of variation not able to be modelled by covariate information, and these models lead naturally to Bayesian formulations using prior distributions for the variance components. We examine implications for specifying scale parameters in hierarchical models.

Online publication date: Mon, 04-Jun-2007

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Services Technology and Management (IJSTM):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com